Access to water and sanitation

Bubble Chart Facet

Photo by Bradford Zak on Unsplash

We shall not defeat any of the infectious diseases that plague the developing world
until we have also won the battle for safe drinking water, sanitation, and basic health care…
— Kofi Annan

Lack of access to safely managed drinking water and sanitation services has a significant effect on people’s health. The impact on child mortality rates is devastating, causing death from diseases due to poor sanitation, poor hygiene, or unsafe drinking water. The bubble chart is a perfect candidate to compare the services, so if we facet by year will we see any progress?

Ingest the data

safely managed drinking water and sanitation services

Proportion of population using safely managed drinking water services
Proportion of population using safely managed sanitation services
Total population
Years 2000 to 2020

# file path parameter
url_root <- "https://raw.githubusercontent.com/UN-AVT/kamino-source/main/sources/0-shared/data/"
url_file <- "access-to-water-and-sanitation/access-to-water-and-sanitation-data.csv"
url <- paste0(url_root, url_file)

df <- read.csv(url)
df

Wrangle the data

select, separate, enrich, clean…

# Select only the fields we need
dfs <- select(df, c("REF_AREA.Geographic.area", "INDICATOR.Indicator", "TIME_PERIOD.Time.period", "OBS_VALUE.Observation.Value")) # , "UNIT_MEASURE.Unit.of.measure"
dfs <- dfs %>% separate(REF_AREA.Geographic.area, c("ISO", "GEOAREA"), sep = ': ')
#dfs

# Enrich with continent field
dfs$continent <- countrycode(dfs$ISO, origin = 'iso3c', destination = 'continent')
# dfs$region <- countrycode(dfs$ISO, origin = 'iso3c', destination = 'un.region.name')
# dfs$subregion <- countrycode(dfs$ISO, origin = 'iso3c', destination = 'un.regionintermediate.name')
#dfs

# Transform from long to wide
dfs_wide <- spread(dfs, INDICATOR.Indicator, OBS_VALUE.Observation.Value)
#dfs_wide

# Remove incomplete cases
df_complete <- dfs_wide[complete.cases(dfs_wide), ]

# Simplify the column names
df_complete <- rename(df_complete, 
                      YEAR = TIME_PERIOD.Time.period, 
                      POPULATION = "DM_POP_TOT: Total population", 
                      SANITATION = "WS_PPL_S-SM: Proportion of population using safely managed sanitation services", 
                      WATER = "WS_PPL_W-SM: Proportion of population using safely managed drinking water services")

# Make sure visual variables are numeric
df_complete$POPULATION <- as.integer(df_complete$POPULATION)
df_complete$SANITATION <- as.numeric(df_complete$SANITATION)
df_complete$WATER <- as.numeric(df_complete$WATER)
df_complete

# Check entries for color palette
#unique(df_complete$continent)

Visual Mapping

year, water, sanitation, population, continent

# Sort so largest bubbles will be on bottom
df_sorted <- df_complete[order(df_complete$POPULATION, decreasing = TRUE),]

# theme parameters
theme_dark_opts <- theme(
  text = element_text(family = "inconsolata"), 
  legend.position = "none",
  legend.title = element_blank(),
  # axis.text = element_blank(),
  axis.line = element_blank(),
  # axis.ticks = element_blank(),
  # axis.title = element_blank(),
  # panel.grid = element_blank(),
  # panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  # panel.background=element_rect(fill="white", colour="white"),
  panel.border = element_blank(),
  # plot.background = element_blank(),
  strip.text.x = element_text(size=14, face="bold"),
  # strip.text.y = element_text(size=12, face="bold"),
  strip.background = element_blank()
)

continent_palette <- c("Europe" = "#ffe330", "Asia" = "#ff7d7b", "Americas" = "#c5ff2d", "Africa" = "#21cccc", "Oceania" = "#ff9dee")

v1 <- ggplot(df_sorted) +
    expand_limits(x = 0, y = 0) +
    geom_point_interactive(aes(x = WATER, 
                 y = SANITATION, 
                 size = POPULATION, 
                 fill = continent,
                 tooltip = paste0("Country: ", GEOAREA, "\n", "Population: ", POPULATION, "\n", "Water: ", WATER, "%\n", "Sanitation: ", SANITATION, "%"), data_id = row.names(df_complete)), 
                 shape = 21, color='#ffffff', alpha = 0.8 ) +
    scale_size(range = c(0, 15)) +
    scale_fill_manual(values = continent_palette) +
    labs(x="WATER",
         y="SANITATION",
         title = "CLEAN WATER AND SANITATION", 
         subtitle="Access to water and sanitation 2000-2020") +
    facet_wrap(~ YEAR, ncol=3, scales="fixed" ) +
    dark_theme_bw() +
    theme_dark_opts

girafe(ggobj = v1, width_svg = 13, height_svg = 24,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

References

The citations and data sources used for this case

Data Source: Unicef, GO